Set subgroup acceptance criteria and investigate: Before | AIGP
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Set subgroup acceptance criteria and investigate: Before deployment, which evaluation decision is most

AIGP Understanding How to Govern AI Development Medium

Overall accuracy hides unequal missed-failure rates, requiring subgroup-specific acceptance criteria and contextual investigation.

The question

An energy-maintenance model correctly prioritizes 96% of work orders overall, but its missed-failure rate is 18% for contractors serving remote sites and 4% elsewhere. Before deployment, which evaluation decision is most defensible?

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  1. Set subgroup acceptance criteria and investigate remote-site conditions. ✓
    Subgroup error rates should affect acceptance, while investigation can determine whether data, operating conditions, or workflow changes explain the disparity.
  2. Track remote-site errors after deployment.
    Monitoring is useful, but it does not resolve the observed subgroup disparity before deployment approval.
  3. Publish both rates while keeping the overall target as the only approval criterion.
    Reporting the disparity without using it in acceptance allows the aggregate target to mask operational risk for remote sites.
  4. Approve on overall accuracy alone.
    The aggregate result conceals a materially higher missed-failure rate for remote-site contractors.
The trap
When subgroup errors differ materially, treat that disparity as an acceptance question, not merely a reporting detail.

How to remember it

Overall accuracy hides unequal missed-failure rates, requiring subgroup-specific acceptance criteria and contextual investigation.

How many of these would you get right?

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